As a form of optimal estimator characterized by recursive evaluation, the Kalman filter (KF) (Bar-Shalom, et al, 2001; Brown and Hwang, 1997, Gelb, 1974; Grewal & Andrews, 2001) has been shown to be the filter that minimizes the variance of the estimation mean square error (MSE) and has been widely applied to the navigation sensor fusion. Nevertheless, in Kalman filter designs, the divergence d...

Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle di...

This paper introduces a recursive algorithm of Kalman filter for digital predistorter parameters extraction based on memory polynomials predistorter model. The predistorter model is firstly formulated as linear regression expression. Then we derive the state-space equation of the model and attain the steps of the algorithm. Finally, the accuracy and stability of the algorithm is proved by simul...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

Control of a class of uncertain nonlinear systems, which estimates unavailable state variables, is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include a strong algorithm to estimate attitude, based on discrete extended Kalman filter combined with a continuous extended Kalman ...

Abstract: This paper presents an application of nonlinear filtering techniques for the tracking control design of tracked mobile robot under slip condition. The slip is represented only by the longitudinal wheels slip that is described by just an unknown parameter. The extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF) are used to estimate the states of...

Aiming at the multi-source heterogeneous of target tracking system information. On the basis of "current" statistical model, this paper researches the unscented Kalman filter information fusion method and analyzes its mathematical model. According to the mathematic model researching of optimal estimation real time tracking algorithm, it be able to describe and process the sensor information of ...

This paper presents a novel Kalman-filter-based approach for approximately enforcing a linear constraint in adaptive control. One application is that this leads to alternative forms for well-known modification terms such as e modification. It is shown that employing this approach does not increase the theoretical guaranteed ultimate bounds for the closed-loop error signals of an existing adapti...

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value...